SoroushVahidi/scidocs huggingface dataset:
Finding time period-based most frequent path in big trajectory data
(2013)

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. In this paper, we study a new path finding query which finds the most frequent path (MFP) during user-specified time periods in large-scale historical trajectory data. We refer to this query as time period-based MFP (TPMFP). Specifically, given a time period T, a source v_s and a destination v_d, TPMFP searches the MFP from v_s to v_d during T. Though there exist several proposals on defining MFP, they only consider a fixed time period. Most importantly, we find that none of them can well reflect people's common sense notion which can be described by three key properties, namely suffix-optimal (i.e., any suffix of an MFP is also an MFP), length-insensitive (i.e., MFP should not favor shorter or longer paths), and bottleneck-free (i.e., MFP should not contain infrequent edges). The TPMFP with the above properties will reveal not only common routing preferences of the past travelers, but also take the time effectiveness into consideration. Therefore, our first task is to give a TPMFP definition that satisfies the above three properties. Then, given the comprehensive TPMFP definition, our next task is to find TPMFP over huge amount of trajectory data efficiently. Particularly, we propose efficient search algorithms together with novel indexes to speed up the processing of TPMFP. To demonstrate both the effectiveness and the efficiency of our approach, we conduct extensive experiments using a real dataset containing over 11 million trajectories.
VisComplete: Automating Suggestions for Visualization Pipelines
(2008)

Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus-automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.
Safe and Nested Endgame Solving for Imperfect-Information Games
(2017)

In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it in individual subgames. This is referred to as subgame solving. We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold’em poker.
Understanding and Constructing Shared Spaces with Mixed-Reality Boundaries
(1998)

We propose an approach to creating shared mixed realities based on the construction of transparent boundaries between real and virtual spaces. First, we introduce a taxonomy that classifies current approaches to shared spaces according to the three dimensions of transportation, artificiality, and spatiality. Second, we discuss our experience of staging a poetry performance simultaneously within real and virtual theaters. This demonstrates the complexities involved in establishing social interaction between real and virtual spaces and motivates the development of a systematic approach to mixing realities. Third, we introduce and demonstrate the technique of mixed-reality boundaries as a way of joining real and virtual spaces together in order to address some of these problems.
Design and Implementation of T-junction Triangular Microstrip Patch Antenna Array for Wireless Applications
(2016)

Abstract— This paper describes 2 x 2 triangular microstrip patch antenna using T-junction with quarter wave transformer. By regulating the distance in patch antenna and adjusting feed position, bandwidth can be obtained and by using an array, directivity is enhanced. The requirement of large bandwidth, high directivity, and minimum size leads to the design of 2 x 2 triangular microstrip patch antenna array feeding with T-junction network operate at 5.5 GHz. An antenna designed on an FR4 substrate that had a dielectric constant (r) 4.4, a loss tangent 0.02 and thickness of 1.6 mm. Simulated results showed that designed antenna has directivity 12.91 dB and bandwidth 173 MHz with VSWR 1.07 using T-junction feeding network. The proposed 2 x 2 triangular array has the benefit of light weight, simplicity of fabrication, single layer structure, and high directivity. Keyword Bandwidth, Corporate feeding, Return Loss, T-junction, VSWR.
Canine responses to hypoglycemia in patients with type 1 diabetes.
(2008)

OBJECTIVE
Anecdotal evidence suggests that domestic dogs may be able to detect hypoglycemia in their human caregivers; scientific investigation of this phenomenon, however, is sorely lacking. This study thus aimed to investigate how pet dogs respond to the hypoglycemic episodes of their owners with type 1 diabetes.


METHODS
Two hundred and twelve dog owners (64.2% female) with medically diagnosed type 1 diabetes participated in the study. All participants owned at least 1 dog. Each person completed a purpose-designed questionnaire developed to collect information on their dogs' responses (if any) to their hypoglycemic episodes.


RESULTS
One hundred and thirty-eight (65.1%) respondents indicated that their dog had shown a behavioral reaction to at least one of their hypoglycemic episodes, with 31.9% of animals reacting to 11 or more events. Canine sex, age, breed status, and length of pet ownership were unrelated to hypoglycemia-response likelihood. Thirty-six percent of the sample believed that their dogs reacted most of the times they went "low"; 33.6% indicated that their pets reacted before they themselves were aware they were hypoglycemic. Dogs' behavioral responses to their owners' hypoglycemic episodes varied. Most animals behaved in a manner suggestive of attracting their owners' attention, for example, vocalizing (61.5%), licking them (49.2%), nuzzling them (40.6%), jumping on top of them (30.4%), and/or staring intently at their faces (41.3%). A smaller proportion showed behavioral responses suggestive of fear, including trembling (7.2%), running away from the owner (5.1%), and/or hyperventilating (2.2%).


CONCLUSIONS
The findings suggest that behavioral reactions to hypoglycemic episodes in pet owners with type 1 diabetes commonly occur in untrained dogs. Further research is now needed to elucidate the mechanism(s) that dogs use to perform this feat.
Unsupervised pattern mining from symbolic temporal data
(2007)

We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data. In particular we distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. The mining paradigms and the robustness of many proposed approaches are compared to aid the selection of the appropriate method for a given problem. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. For univariate data and limited gaps suffix tree methods are more efficient. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. For time interval data with precise start and end points the relations of Allen can be used to formulate patterns. The recently proposed Time Series Knowledge Representation is more robust on noisy data and offers an alternative semantic that avoids ambiguity and is more expressive. For both pattern languages efficient mining algorithms have been proposed.
Circuit techniques for reducing the effects of op-amp imperfections: autozeroing, correlated double sampling, and chopper stabilization
(1996)

In linear IC's fabricated in a low-voltage CMOS technology, the reduction of the dynamic range due to the dc offset and low,frequency noise of the ampli$ers becomes increasingly significant. Also, the achievable aniplijier gain is often quite low in such a technology, since cascoding may not be a practical circuit option due to the resulting reduction of the output signal swing. In this paper, some old and some new circuit techniques will be described for the compensation of the amplifier most important nonideal effects including the noise (mainly thermal and l l f noise), the input-referred dc offset voltage, as well as the finite gain resulting in a nonideal virtual ground at the input.
Only Aggressive Elephants are Fast Elephants
(2012)

Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider’s orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.
Fast and practical indexing and querying of very large graphs
(2007)

Many applications work with graph-structured data. As graphs grow in size, indexing becomes essential to ensure sufficient query performance. We present the GRIPP index structure (GRaph Indexing based on Pre- and Postorder numbering) for answering reachability queries in graphs.
 GRIPP requires only linear time and space. Using GRIPP, we can answer reachability queries on graphs with 5 million nodes on average in less than 5 milliseconds, which is unrivaled by previous methods. We evaluate the performance and scalability of our approach on real and synthetic random and scale-free graphs and compare our approach to existing indexing schemes. GRIPP is implemented as stored procedure inside a relational database management system and can therefore very easily be integrated into existing graph-oriented applications.
Computational augmented reality eyeglasses
(2013)

In this paper we discuss the design of an optical see-through head-worn display supporting a wide field of view, selective occlusion, and multiple simultaneous focal depths that can be constructed in a compact eyeglasses-like form factor. Building on recent developments in multilayer desktop 3D displays, our approach requires no reflective, refractive, or diffractive components, but instead relies on a set of optimized patterns to produce a focused image when displayed on a stack of spatial light modulators positioned closer than the eye accommodation distance. We extend existing multilayer display ray constraint and optimization formulations while also purposing the spatial light modulators both as a display and as a selective occlusion mask. We verify the design on an experimental prototype and discuss challenges to building a practical display.
Personal Recommendation as Link Prediction using a Bipartite Graph Kernel
(2012)

We implement a personal recommendation system on the Yelp academic dataset using a novel algorithm proposed by [Li and Chen., 2012]. By constructing a bipartite userbusiness graph where positive reviews are edges between nodes, we pose the recommendation problem as link prediction. More specifically, we construct a kernel which combines the information from both the node features and local graph structure. This kernel is then put into a one-class support vector machine, which generates an ordering of likely user-business links. We downsample the graph and tune the parameters of our algorithm (via cross validation). In what follows we provide a summary of the Yelp data, describe the recommendation algorithm in detail, and present the results of our implementation.
Optimizing F-Measure with Support Vector Machines
(2003)

Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high F-measure, although this particular application of SVMs has not been rigorously examined. We provide significant and new theoretical results that support this popular heuristic. Specifically, we demonstrate that with the right parameter settings SVMs approximately optimize F-measure in the same way that SVMs have already been known to approximately optimize accuracy. This finding has a number of theoretical and practical implications for using SVMs in F-measure optimization.
Decision Support Systems Using Intelligent Paradigms
(2004)

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this paper, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.
Compact Dual-Band Antenna With Electronic Beam-Steering and Beamforming Capability
(2011)

A low-cost compact dual-band antenna that can achieve electronic beam steering in the horizontal plane across a range from 0° to 360° and adaptive beamforming is presented. Multiple radiation patterns can be generated by the antenna for interference canceling. To reduce the size and cost, the antenna employs the configuration of electronically steerable parasitic array radiators (ESPARs). The parasitic elements are 12 folded monopole antennas, which surround a short monocone antenna. The short monocone antenna serves as the driven element. Each folded monopole is loaded with a p-i-n diode, and by controlling the dc voltages applied to the p-i-n diodes, the antenna can achieve electronic beam steering and adaptive beamforming. To prove the concept, a prototype antenna was developed whose frequency bands are 1.8-2.2 and 2.85-3.15 GHz. The height of the antenna has been reduced to 0.12 λ at 1.8 GHz.
Effects of bacterial inoculants on the indigenous microbiome and secondary metabolites of chamomile plants
(2014)

Plant-associated bacteria fulfill important functions for plant growth and health. However, our knowledge about the impact of bacterial treatments on the host's microbiome and physiology is limited. The present study was conducted to assess the impact of bacterial inoculants on the microbiome of chamomile plants Chamomilla recutita (L.) Rauschert grown in a field under organic management in Egypt. Chamomile seedlings were inoculated with three indigenous Gram-positive strains (Streptomyces subrutilus Wbn2-11, Bacillus subtilis Co1-6, Paenibacillus polymyxa Mc5Re-14) from Egypt and three European Gram-negative strains (Pseudomonas fluorescens L13-6-12, Stenotrophomonas rhizophila P69, Serratia plymuthica 3Re4-18) already known for their beneficial plant-microbe interaction. Molecular fingerprints of 16S rRNA gene as well as real-time PCR analyses did not show statistically significant differences for all applied bacterial antagonists compared to the control. In contrast, a pyrosequencing analysis of the 16S rRNA gene libraries revealed significant differences in the community structure of bacteria between the treatments. These differences could be clearly shown by a shift within the community structure and corresponding beta-diversity indices. Moreover, B. subtilis Co1-6 and P. polymyxa Mc5Re-14 showed an enhancement of the bioactive secondary metabolite apigenin-7-O-glucoside. This indicates a possible new function of bacterial inoculants: to interact with the plant microbiome as well as to influence the plant metabolome.
Pitch Elevation in Male-to-female Transgender Persons-the Würzburg Approach.
(2017)

OBJECTIVES
The present study reports objective and subjective voice results of Wendler's glottoplasty modified by Hagen.


STUDY DESIGN
This is an outcomes research study.


METHODS
A total of 21 patients underwent Wendler's glottoplasty modified by Hagen. Parameters in the follow-up session were laryngoscopy, voice range profile, Voice Handicap Index, Life Satisfaction Questionnaire, and a visual analog scale for individual satisfaction with the voice.


RESULTS
The fundamental frequency was elevated into the typical female fundamental frequency range. Furthermore, an elevation of the lower frequency limit was shown without a reduction of the frequency range. About one third of the population feels affected by the restricted dynamic range. This change of the vocal pitch is seen as part of the voice feminization by some of the patients. The Dysphonia Severity Index as a marker for voice quality was unchanged. Subjective satisfaction with the voice showed a strong correlation with the individual elevation of the pitch.


CONCLUSION
Wendler's glottoplasty modified by Hagen is an effective and low-risk method of raising the vocal pitch of male-to-female transgender persons. However, elevated Scores of the Voice Handicap Index indicated that in everyday life, transgender persons continue to feel handicapped because of their voice. Another indicator for the lack of social acceptance and integration is the reduced general life satisfaction in the Life Satisfaction Questionnaire especially in the domain "friends, acquaintances, relatives." Therefore, a better multidisciplinary therapy concept for voice feminization is necessary.
Zero-Bending Piezoelectric Micromachined Ultrasonic Transducer (pMUT) With Enhanced Transmitting Performance
(2015)

A piezoelectric micromachined ultrasonic transducer (pMUT) has enabled numerous exciting ultrasonic applications. However, residual stress and initial buckling may worsen the transmitting sensitivity of a pMUT, and also limit its application and commercialization. In this paper, we report a new innovative pMUT with a perfectly flat membrane, i.e., zero-bending membrane. Leveraging on the stress-free AlN thin film, framelike top electrode layout, and integrated vacuum cavity, the initial deflection of suspended membrane is significantly suppressed to only 0.005%. The transmitting sensitivity of the zero-bending pMUT is measured as 123 nm/V at a resonant frequency of 2.21 MHz, which is 450% higher than that of the reference pMUT with slightly non-zero initial deflection. Compared with the simulation results, the measured data of zero-bending pMUT achieve 94.5% of its ideal transmitting sensitivity. It is solid evidence that our approach is an effective and reliable way to overcome the residual stress and the initial buckling issue.
Sparse reconstruction cost for abnormal event detection
(2011)

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.
Compressed Representations for Web and Social Graphs 1
(2012)

Compressed representations have become effective to store and access large Web and social graphs, in order to support various graph querying and mining tasks. The existing representations exploit various typical patterns in those networks and provide basic navigation support. In this paper we obtain unprecedented results by finding “dense subgraph” patterns and combining them with techniques such as node orderings and compact data structures. On those representations we support out-neighbor and out/in-neighbor queries, as well as mining queries based on dense subgraphs. First, we propose a compression scheme for Web graphs that reduces edges by representing dense subgraphs with “virtual nodes”; over this scheme we apply node orderings and other compression techniques. With this approach we match the best current compression ratios that support out-neighbor queries (i.e., nodes pointed from a given node), using 1.0–1.8 bits per edge (bpe) on large Web graphs, and retrieving each neighbor of a node in 0.6–1.0 microseconds (μsec). When supporting both outand in-neighbor queries, instead, our technique generally offers the best time when using little space. If the reduced graph, instead, is represented with a compact data structure that supports bidirectional navigation, we obtain the most compact Web graph representations (0.9–1.5 bpe) that support out/in-neighbor navigation, yet the time per neighbor extracted raises to around 5–20 μsec. We also propose a compact data structure that represents dense subgraphs without using virtual nodes. It allows us to recover out/in-neighbors and answer other more complex queries on the dense subgraphs identified. This structure is not competitive on Web graphs, but on social networks it achieves 4–13 bpe and 8–12 μsec per out/in-neighbor retrieved, which improves upon all existing representations.